Consumer perspectives on mHealth for weight loss: a review of qualitative studies

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Abstract

Introduction With increasing development and use of mobile health (mHealth) interventions for weight loss in overweight and obese populations, it is timely to gain greater insight into consumer experience with these technologies. The aims of this review were to identify common themes across studies that included user preferences for mHealth intervention for weight loss. Methods The databases PubMed (Medline), CINAHL, Web of Science, and Embase were searched for relevant qualitative studies on mHealth for weight loss. Searches were conducted in May 2016. Results Several common high preference themes were identified relating to simple and attractive apps that allowed for self-monitoring with feedback. The five key themes concerning text messages for weight loss involved a careful consideration of personalization, message tone, structure, frequency and content. Key optimization themes for weight loss apps were personalization, simplicity with appeal and engagement/entertainment. Common identified benefits of mHealth for weight loss included self-monitoring, goal setting, feedback, ability to motivate, educate, and remind. Common barriers users identified were related to technological and psychological issues as well as message overload/inappropriate timing of messages. Conclusion When planning an mHealth weight loss intervention, critical factors are the message tone, structure and the frequency of message delivery. Personalization also seems to be important. Designing simple apps while still ensuring that they engage the user is also essential. Additionally, it seems important to tailor the content in accordance with different target group demographic preferences. The successful reach and adoption of mHealth interventions requires minimizing perceived barriers and maximizing perceived benefits.

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A review of efficacious technology-based weight-loss interventions: five key components.

Cheryl Gore-Felton, Anna Khaylis, Themis Yiaslas … (2010)

Obesity is highly prevalent among American adults and has negative health and psychosocial consequences. The purpose of this article was to qualitatively review studies that used technology-based interventions for weight loss and to identify specific components of these interventions that are effective in facilitating weight loss. We conducted a narrow, qualitative review, focusing on articles published in the last 10 years that used an experimental or pre/posttest design and used a technology-based intervention for weight loss. Among the 21 studies reviewed, we identified the following five components that we consider to be crucial in technology-based weight-loss interventions that are successful in facilitating weight loss: self monitoring, counselor feedback and communication, social support, use of a structured program, and use of an individually tailored program. Short-term results of technologically driven weight-loss interventions using these components have been promising, but long-term results have been mixed. Although more longitudinal studies are needed for interventions implementing these five components, the interface of technology and behavior change is an effective foundation of a successful, short-term weight-loss program and may prove to be the basis of long-term weight loss.

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Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults

Mashfiqui Rabbi, Angela Pfammatter, Mi Zhang … (2015)

Background A dramatic rise in health-tracking apps for mobile phones has occurred recently. Rich user interfaces make manual logging of users’ behaviors easier and more pleasant, and sensors make tracking effortless. To date, however, feedback technologies have been limited to providing overall statistics, attractive visualization of tracked data, or simple tailoring based on age, gender, and overall calorie or activity information. There are a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement. Objective MyBehavior, a mobile phone app, was designed to process tracked physical activity and eating behavior data in order to provide personalized, actionable, low-effort suggestions that are contextualized to the user’s environment and previous behavior. This study investigated the technical feasibility of implementing an automated feedback system, the impact of the suggestions on user physical activity and eating behavior, and user perceptions of the automatically generated suggestions. Methods MyBehavior was designed to (1) use a combination of automatic and manual logging to track physical activity (eg, walking, running, gym), user location, and food, (2) automatically analyze activity and food logs to identify frequent and nonfrequent behaviors, and (3) use a standard machine-learning, decision-making algorithm, called multi-armed bandit (MAB), to generate personalized suggestions that ask users to either continue, avoid, or make small changes to existing behaviors to help users reach behavioral goals. We enrolled 17 participants, all motivated to self-monitor and improve their fitness, in a pilot study of MyBehavior. In a randomized two-group trial, investigators randomly assigned participants to receive either MyBehavior’s personalized suggestions (n=9) or nonpersonalized suggestions (n=8), created by professionals, from a mobile phone app over 3 weeks. Daily activity level and dietary intake was monitored from logged data. At the end of the study, an in-person survey was conducted that asked users to subjectively rate their intention to follow MyBehavior suggestions. Results In qualitative daily diary, interview, and survey data, users reported MyBehavior suggestions to be highly actionable and stated that they intended to follow the suggestions. MyBehavior users walked significantly more than the control group over the 3 weeks of the study (P=.05). Although some MyBehavior users chose lower-calorie foods, the between-group difference was not significant (P=.15). In a poststudy survey, users rated MyBehavior’s personalized suggestions more positively than the nonpersonalized, generic suggestions created by professionals (P<.001). Conclusions MyBehavior is a simple-to-use mobile phone app with preliminary evidence of efficacy. To the best of our knowledge, MyBehavior represents the first attempt to create personalized, contextualized, actionable suggestions automatically from self-tracked information (ie, manual food logging and automatic tracking of activity). Lessons learned about the difficulty of manual logging and usability concerns, as well as future directions, are discussed. Trial Registration ClinicalTrials.gov NCT02359981; https://clinicaltrials.gov/ct2/show/NCT02359981 (Archived by WebCite at http://www.webcitation.org/6YCeoN8nv).

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How can weight-loss app designers' best engage and support users? A qualitative investigation.

Jason Tang, Charles Abraham, Elena Stamp … (2015)

This study explored young adults' experiences of using e-health internet-based computer or mobile phone applications (apps) and what they valued about those apps.